In this study, we aim to address this problem and present the potential solutions. Figure REF  illustrates the overall structure of this paper.
Steganography is an advanced skill and communication method that allows hidden secret messages to be sent via an innocuous multimedia file [1]}. The most common multimedia communications involve images, audios, videos, even text files, or internet protocols [2]}. The cover multimedia, with secret messages embedded in, is called stego multimedia. The goal of steganography is to make the stego multimedia and the associated cover look as identical as possible. Otherwise, the risk of detection would be high.
Steganalysis is a challenging field because all the different steganography schemes must be recognized using different steganalysis algorithms. Researchers have proposed steganalysis algorithms for individual steganography schemes. To this end, first, they extract important features based on preferred extraction
strategies [3]}. The goal of these features is to provide a distinguishing border between stego images and cover images.
Further, researchers propose a universal steganalysis to detect stego images from cover ones without considering the steganographic scheme, known or unknown, used to embed the hidden messages. A large number of research studies have proposed to improve steganalysis performance with a large number of features like CC-C300 with 48600 features [4]}, in which the the authors aimed to identify the goodness of utilizing high-dimensional features, together with ensemble classifiers by comparing them to selected existing steganalysis algorithms. Another example is PHARM with 12600 features [5]}, in which the authors identified an important
discovery that in the pixels of a decompressed JPEG image are not shift invariant and their statistical characteristics heavily rely on their position in the filtering (e.x. 8*8 grid). These are examples of the universal (blind) approach. However, to do universal steganalysis like those mentioned above, we need to extract as many features as possible, which is also the main disadvantage of it. Because this leads to the problem of the Curse of dimensionality (CoD). CoD is generated upon extracting or gathering too much information.
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